The present invention relates to extracting a centerline representation of vascular branches in medical images, and more particularly, to automated centerline extraction of vascular branches in medical images via optimal paths in computational flow fields.
Automatic segmentation of coronary arteries in Computed Tomography Angiography (CTA) facilitates the diagnosis, treatment, and monitoring of coronary artery diseases. An important step in coronary artery segmentation is to extract a curve along the center of the coronary artery referred to as a centerline. A centerline representation is important for the visualization of the artery through a curved planar reformatting. A centerline is also useful to support lumen segmentation methods for quantitative assessments, such as stenosis grading or CT based Fractional Flow Reserve (FFR) measurements.
Coronary arteries constitute only a small portion of a large CTA volume because of their thin and longitudinal geometry, and segmentation of coronary arteries is not an easy task due to nearby heart tissues and other vessels such as veins. Most existing centerline tracing techniques for extracting coronary artery centerlines are based on minimal path extraction algorithms that are very prone to making shortcuts through non-coronary structures due to imaging artifacts and contrast variation, severe pathologies, and sharp or fuzzy bifurcations. Methods have been proposed to compute “vesselness” masks with very high sensitivity. However, such vesselness masks may still include false positive voxels belonging to background or nearby structures, causing the centerline extraction to make connections through false positive voxels.